def test_min_max_scaler_multi_deserializer(self):
extract_features = ['a', 'b']
feature_extractor = FeatureExtractor(
input_scalars=['a', 'b'],
output_vector='extracted_multi_outputs',
output_vector_items=["{}_out".format(x) for x in extract_features])
scaler = MinMaxScaler()
scaler.mlinit(prior_tf=feature_extractor,
output_features=['a_scaled', 'b_scaled'])
scaler.fit(self.df[['a']])
scaler.serialize_to_bundle(self.tmp_dir, scaler.name)
# Deserialize the MinMaxScaler
node_name = "{}.node".format(scaler.name)
min_max_scaler_tf = MinMaxScaler()
min_max_scaler_tf.deserialize_from_bundle(self.tmp_dir, node_name)
# Transform some sample data
res_a = scaler.transform(self.df[['a', 'b']])
res_b = min_max_scaler_tf.transform(self.df[['a', 'b']])
self.assertEqual(res_a[0][0], res_b[0][0])
self.assertEqual(res_a[0][1], res_b[0][1])
self.assertEqual(scaler.name, min_max_scaler_tf.name)
self.assertEqual(scaler.op, min_max_scaler_tf.op)
def test_min_max_scaler_deserializer(self):
scaler = MinMaxScaler()
scaler.mlinit(input_features=['a'], output_features=['a_scaled'])
scaler.fit(self.df[['a']])
scaler.serialize_to_bundle(self.tmp_dir, scaler.name)
# Deserialize the MinMaxScaler
node_name = "{}.node".format(scaler.name)
min_max_scaler_tf = MinMaxScaler()
min_max_scaler_tf.deserialize_from_bundle(self.tmp_dir, node_name)
# Transform some sample data
res_a = scaler.transform(self.df[['a']])
res_b = min_max_scaler_tf.transform(self.df[['a']])
self.assertEqual(res_a[0], res_b[0])
self.assertEqual(scaler.name, min_max_scaler_tf.name)
self.assertEqual(scaler.op, min_max_scaler_tf.op)